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Paper Citation Record · LEDGER

Language is All a Graph Needs

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2308.07134.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2308.07134 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:24:05.626632Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-19T10:42:15.272924Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d840b0d2-a0f6-49e5-a9c2-ff7160ef21da · inbound

MLaGA: Multimodal Large Language and Graph Assistant cites this paper.

MLaGA: Multimodal Large Language and Graph Assistant Language is All a Graph Needs

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T11:26:43.571333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:26:43.571333Z digest=sha256:87aced4f000b9c9578e9f2dfdde79591033cb75567b981e35ab5dd1d2fd7a9fb

Observation 3192eb84-3a0a-49dd-b5bb-2398d226b63a · inbound

DistRAG: Towards Distance-Based Spatial Reasoning in LLMs cites this paper.

DistRAG: Towards Distance-Based Spatial Reasoning in LLMs Language is All a Graph Needs

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T11:07:55.008020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:07:55.008020Z digest=sha256:105cc44be75339a1c2f99fd123f5b2a4648706ac0e59da101a5cb8d3ffd7c826

Observation f911c81c-7a18-4a35-829e-e233715a9bc6 · inbound

Are Large Language Models Good Temporal Graph Learners? cites this paper.

Are Large Language Models Good Temporal Graph Learners? Language is All a Graph Needs

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-07T11:09:45.389027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:09:45.389027Z digest=sha256:c2c622d3fdf7b27e8d02483cfcd67ef24ac27180f926c4d4beec0c50d54ed10d

Observation cc088158-b27e-45fe-980d-b54d14bfdd18 · inbound

Masked Language Models are Good Heterogeneous Graph Generalizers cites this paper.

Masked Language Models are Good Heterogeneous Graph Generalizers Language is All a Graph Needs

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-07T06:04:17.194313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:04:17.194313Z digest=sha256:c0ea7c2c7b242bb4273dfc122769d1bf5ee844f13cdcfc9e019f9c3d74f2a391

Observation a2f7338d-dd96-459b-b405-f7e8708d8eb1 · inbound

No Data? No Problem: Synthesizing Security Graphs for Better Intrusion Detection cites this paper.

No Data? No Problem: Synthesizing Security Graphs for Better Intrusion Detection Language is All a Graph Needs

Reference 69

Resolution
verified exact
arxiv_id, observed 2026-05-19T10:42:15.274753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T10:38:03.311357Z digest=sha256:cd6b4e9d6eaa8f805625c4878309cf368d0606562898bc57db1beb83cfd5d58e

Observation 8ff1dd83-ce1d-4bc4-9e3c-7db73604027b · inbound

Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment cites this paper.

Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment Language is All a Graph Needs

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-07T05:44:53.335317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:44:53.335317Z digest=sha256:0271d2e66e9eedde6a9aa36171b2a41fcb94eaffcf11c5a4d7c414b11e961605

Observation b5b1e1e8-d95f-4c22-b61a-b6efa0458953 · inbound

Graph Prompting for Graph Learning Models: Recent Advances and Future Directions cites this paper.

Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Language is All a Graph Needs

Reference 125

Resolution
unresolved
no resolver link, observed 2026-08-07T05:17:17.276012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:17:17.276012Z digest=sha256:05227b8fadebeb336562cd9ecf2c30f6ae82069757c931e7dd108ba1c235ef6d

Observation c1b7ae42-d5f1-4534-ab8a-f831242428d7 · inbound

NOCL: Node-Oriented Conceptualization LLM for Graph Tasks without Message Passing cites this paper.

NOCL: Node-Oriented Conceptualization LLM for Graph Tasks without Message Passing Language is All a Graph Needs

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T13:24:05.626632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:24:05.626632Z digest=sha256:12c02f46e0f6aa0353dcfc3bc23969243d9b693c61ffe1c8112e359f0c6d6cc9

Observation 74e83e17-cfd8-48b3-99c1-22c5b06dc662 · inbound

Graph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learning cites this paper.

Graph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learning Language is All a Graph Needs

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-07T04:33:14.909196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:33:14.909196Z digest=sha256:e2db262895f7820803a56b3a53f7319cd90e470c736a27f66b07fd3582f77ed0

Observation 8c4731f8-d4f3-4eb1-8a61-51d310adda4e · inbound

TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks cites this paper.

TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks Language is All a Graph Needs

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T04:07:00.049108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:07:00.049108Z digest=sha256:f8074a79ab295cda457e882d86d8816e60fb814e602020baebfb672cb4c8c716

Observation b22ed1b9-1ffd-4212-8728-a55c91bfc1ee · inbound

Quantizing Text-attributed Graphs for Semantic-Structural Integration cites this paper.

Quantizing Text-attributed Graphs for Semantic-Structural Integration Language is All a Graph Needs

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-06T15:51:12.910996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:51:12.910996Z digest=sha256:fa88fdd51ffead2d72bb497dfd7ad2cc7f57f6315fd91dc13245131ca1030994

Observation a3a59509-418f-4f67-ae18-6a64dddfeb8d · inbound

Harnessing Adaptive Topology Representations for Zero-Shot Graph Question Answering cites this paper.

Harnessing Adaptive Topology Representations for Zero-Shot Graph Question Answering Language is All a Graph Needs

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-05T22:55:20.895252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:55:20.895252Z digest=sha256:376acabf4d32c0d46972d3544d09469947d4302964b3cff1b4cde62d2e94b598

Observation e1170ab9-bc9d-474d-8785-36fda01c39d7 · inbound

Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses cites this paper.

Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses Language is All a Graph Needs

Reference 241

Resolution
unresolved
no resolver link, observed 2026-08-05T15:39:56.472995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:39:56.472995Z digest=sha256:e9a411ae334331d2c5d5a5245a58890297524d9a964801708928df7efccc6cd6

Observation d9a23ee5-3db4-4f64-86d2-3a938928ba33 · inbound

Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning cites this paper.

Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning Language is All a Graph Needs

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-05T12:53:33.325883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T12:53:33.325883Z digest=sha256:8e3076250fe83df03f9e62bbaefdcc46f1359da8681335c53bab18cfd51bc73f

Observation 910b5d1e-3ab1-4a75-ab38-39e1d35690aa · inbound

Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding cites this paper.

Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Language is All a Graph Needs

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-12T11:01:31.318128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-07T16:49:54.542437Z digest=sha256:243dc070530e438c7bdac001fb9a121ee29bde9a2fa4ab6b6757c825739e2014